A Systematic Literature Review on Convolutional Neural Networks applied to Single Board Computers
Resumo
This work consists of systematically reviewing studies involving applications of convolutional neural network (CNN) algorithms implemented in single board computers (SBC) to verify their feasibility in computer vision (CV) applications. Indicating the main parameters for evaluating the performance of SBCs as the processing time (88.24%) and accuracy (29.41%). Furthermore, the most used SBCs and the motivation for choosing such devices are indicated. The study contributes to help in future works involving CNNs in embedded systems, as well as in discussions about architectures involving deep learning applications.
Referências
Ali, A. S., Duman, B., and S¸ en Betül (2020). Benchmark Analysis of Jetson TX2, Jetson Nano and Raspberry PI using Deep-CNN. In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pages 1-5, Ankara, Turkey. IEEE.
Boschi, A., Salvetti, F., Mazzia, V., and Chiaberge, M. (2020). A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge. Machines, 8(3):49.
Calabrese, B., Velázquez, R., Del-Valle-Soto, C., de Fazio, R., Giannoccaro, N. I., and Visconti, P. (2020). Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired. Energies, 13(22):6104.
Curtin, B. H. and Matthews, S. J. (2019). Deep Learning for Inexpensive Image Classification of Wildlife on the Raspberry Pi. In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON), pages 0082-0087, New York, NY, USA. IEEE.
Dayal, A., Paluru, N., Cenkeramaddi, L. R., J., S., and Yalavarthy, P. K. (2021). Design and Implementation of Deep Learning Based Contactless Authentication System Using Hand Gestures. Electronics, 10(2):182.
Deniz, O., Vallez, N., Espinosa-Aranda, J. L., Rico-Saavedra, J. M., Parra-Patino, J., Bueno, G., Moloney, D., Dehghani, A., Dunne, A., Pagani, A., Krauss, S., Reiser, R., Waeny, M., Sorci, M., Llewellynn, T., Fedorczak, C., Larmoire, T., Herbst, M., Seirafi, A., and Seirafi, K. (2017). Eyes of Things. Sensors, 17(5):1173.
Hossain, S. and jin Lee, D. (2019). Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices. Sensors, 19(15):3371.
Kim, W., Jung, W.-S., and Choi, H. K. (2019). Lightweight Driver Monitoring System Based on Multi-Task Mobilenets. Sensors, 19(14):3200.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.
Leroux, S., Bohez, S., Coninck, E. D., Verbelen, T., Vankeirsbilck, B., Simoens, P., and Dhoedt, B. (2017). The cascading neural network: building the Internet of Smart Things. Knowledge and Information Systems (KAIS), 54(3):791-814.
Lindner, T., Wyrwał, D., Białek, M., and Nowak, P. (2020). Face recognition system based on a single-board computer. In 2020 International Conference Mechatronic Systems and Materials (MSM), pages 1-6, Bialystok, Poland. IEEE.
Milioto, A., Lottes, P., and Stachniss, C. (2017). Real-Time Blob-Wise Sugar Beets vs Weed Classification for Monitoring Fields Using Convolutional Neural Networks. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W3(1):41-48.
Nikodem, M., Słabicki, M., Surmacz, T., Mrówka, P., and Dołega, C. (2020). Multi-Camera Vehicle Tracking Using Edge Computing and Low-Power Communication. Sensors, 20(11):3334.
Oliveira, D. C. D. and Wehrmeister, M. A. (2018). Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV. Sensors, 18(7):2244.
Sordillo, S., Cheikh, A., Mastrandrea, A., Menichelli, F., and Olivieri, M. (2021). Customizable Vector Acceleration in Extreme-Edge Computing: A RISC-V Software/Hardware Architecture Study on VGG-16 Implementation. Electronics, 10(4):518.
Boschi, A., Salvetti, F., Mazzia, V., and Chiaberge, M. (2020). A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge. Machines, 8(3):49.
Calabrese, B., Velázquez, R., Del-Valle-Soto, C., de Fazio, R., Giannoccaro, N. I., and Visconti, P. (2020). Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired. Energies, 13(22):6104.
Curtin, B. H. and Matthews, S. J. (2019). Deep Learning for Inexpensive Image Classification of Wildlife on the Raspberry Pi. In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON), pages 0082-0087, New York, NY, USA. IEEE.
Dayal, A., Paluru, N., Cenkeramaddi, L. R., J., S., and Yalavarthy, P. K. (2021). Design and Implementation of Deep Learning Based Contactless Authentication System Using Hand Gestures. Electronics, 10(2):182.
Deniz, O., Vallez, N., Espinosa-Aranda, J. L., Rico-Saavedra, J. M., Parra-Patino, J., Bueno, G., Moloney, D., Dehghani, A., Dunne, A., Pagani, A., Krauss, S., Reiser, R., Waeny, M., Sorci, M., Llewellynn, T., Fedorczak, C., Larmoire, T., Herbst, M., Seirafi, A., and Seirafi, K. (2017). Eyes of Things. Sensors, 17(5):1173.
Hossain, S. and jin Lee, D. (2019). Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices. Sensors, 19(15):3371.
Kim, W., Jung, W.-S., and Choi, H. K. (2019). Lightweight Driver Monitoring System Based on Multi-Task Mobilenets. Sensors, 19(14):3200.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.
Leroux, S., Bohez, S., Coninck, E. D., Verbelen, T., Vankeirsbilck, B., Simoens, P., and Dhoedt, B. (2017). The cascading neural network: building the Internet of Smart Things. Knowledge and Information Systems (KAIS), 54(3):791-814.
Lindner, T., Wyrwał, D., Białek, M., and Nowak, P. (2020). Face recognition system based on a single-board computer. In 2020 International Conference Mechatronic Systems and Materials (MSM), pages 1-6, Bialystok, Poland. IEEE.
Milioto, A., Lottes, P., and Stachniss, C. (2017). Real-Time Blob-Wise Sugar Beets vs Weed Classification for Monitoring Fields Using Convolutional Neural Networks. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W3(1):41-48.
Nikodem, M., Słabicki, M., Surmacz, T., Mrówka, P., and Dołega, C. (2020). Multi-Camera Vehicle Tracking Using Edge Computing and Low-Power Communication. Sensors, 20(11):3334.
Oliveira, D. C. D. and Wehrmeister, M. A. (2018). Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV. Sensors, 18(7):2244.
Sordillo, S., Cheikh, A., Mastrandrea, A., Menichelli, F., and Olivieri, M. (2021). Customizable Vector Acceleration in Extreme-Edge Computing: A RISC-V Software/Hardware Architecture Study on VGG-16 Implementation. Electronics, 10(4):518.
Publicado
16/11/2022
Como Citar
SILVA, Kamilla Taiwhscki B.; SILVA, Cleison Daniel; SANTOS, Rafael Luis.
A Systematic Literature Review on Convolutional Neural Networks applied to Single Board Computers. In: ESCOLA REGIONAL DE ALTO DESEMPENHO NORTE 2 (ERAD-NO2) E ESCOLA REGIONAL DE APRENDIZADO DE MÁQUINA E INTELIGÊNCIA ARTIFICIAL NORTE 2 (ERAMIA-NO2), 2. , 2022, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 17-20.
DOI: https://doi.org/10.5753/erad-no2.2022.228156.
